Is policy learning and online system identification the same?
From the book Reinforcement Learning, An Introduction (R. Sutton, A. Barto):


  The term system identification is used in adaptive control for what we
  call model-learning (e.g., Goodwin and Sin, 1984; Ljung and S
  ̈oderstrom, 1983; Young, 1984).


Model-learning refers to the act of learning the model (environment). Reinforcement Learning can be divided into two types:


Model-based - first we build a model of an environment and then do the control.
Model-free - we do not try to model the behaviour of the environment. 


Policy learning is the act of learning optimal policy. You can do it in two ways:


On-policy learning - learn about the policy $π$ by sampling from the same policy.
Off-policy learning - learn about the policy $π$ from the experience sampled from some other policy (e.g. watching different agent playing a game).